Research On Optimal Control Strategy Of Electric Of Electric Vehicle Drive System

According to energy-saving emission and reduction requirement for the traditional automobile, the revolution of new energy vehicles will be a tendency in the future. Pure electric vehicle is energy conservation and environmental protection. It also has easy industrialization ability and wide attention around the world. Besides, it has been regarded as an important goal in the global governments and automobile enterprises recently. But at the same time, we also need to improve its manufacturing capacity and the level of technology and continue to develop the overall performance of the automobile, which has becoming the main focus of new energy automotive industry. Electric vehicle drive system control strategy optimization is studied in this paper. The specific contents are as follows:(1) The selection of the motor, battery and transmission and the matching of the transmission ratio in the transmission are studied in the electric vehicle drive system;(2) According to the related theories of automobile, drive system dynamic and economic multi-objective model was set up, which contains the motor speed, torque and other important related factors, comprehensively reflect electric vehicles the actual traffic situation, better reflect the actual running effect;(3) Through the related automobile knowledge and the joint establishment of the electric vehicle dynamic mathematical model and economic mathematical model, and under the constraint conditions of the main reducer drive ratio, gear ratio and the two gear transmission ratio of the value;(4) By using the MATLAB software, the based on multi-objective particle swarm optimization algorithm, the electric bus power system of the main reducer transmission ratio, gear transmission ratio and the second block transmission than parameter optimization and iterative 100 times after continued driving range and the maximum points of obtained optimized parameter values;(5) The simulation experiment is carried out in the electric vehicle software ADVISOR and the optimized result is obtained. Compared with the optimization, the corresponding conclusion is obtained as well.